#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
library(knitr)
library(biomaRt)
library(anRichment) ; library(BrainDiseaseCollection)
suppressWarnings(suppressMessages(library(WGCNA)))
Load preprocessed dataset (preprocessing code in 19_10_14_data_preprocessing.Rmd) and clustering (pipeline in 19_10_21_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
clustering_selected = 'DynamicHybrid'
genes_info$Module = genes_info[,clustering_selected]
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
# Correct SFARI Scores
dataset$gene.score = genes_info$gene.score
# Enrichment Analysis
load('./../Data/enrichmentAnalysis.RData')
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}
rm(DE_info, GO_annotations, clusterings, getinfo, mart, dds, GO_neuronal)
To measure the presence of SFARI Genes in a module, at first we were just measuring the percentage of genes in the module that belonge to SFARI, but this approach does not take into account the size of the module, and because of this it doesn’t take into consideration the robustness of the results (it’s easier to get a high percentage of SFARI genes in a small module by chance than in a larger module). Because of this, we chose to use the following approach:
If we assume independence between SFARI Genes and modules, we can calculate the probability of obtaining a proportion of SFARI Genes at least as big as the one found in each module given its size
Notation:
N = Number of genes (16147)
S = Number of SFARI genes (789)
For each module:
n = Number of genes in module
s = Number of SFARI Genes in module
If we interpret the number of genes (\(n\)) in a module as \(n\) random draws without replacement from a finite population of size \(N\), and the number of SFARI genes in the module (\(k\)) as \(k\) successes in those \(n\) draws, where we know that \(N\) contains exactly \(K\) successes, then we can use the Hypergeometric Distribution to calculate the statistical significance of having drawn \(k\) successes out of \(n\) draws, and use this value to select the clusters with the highest enrichment
For Modules with negative correlation to ASD there doesn’t seem to be a relation between this correlation and the enrichment of the SFARI Genes of the module, but for Modules with positive correlation, the higher the correlation, the smaller the enrichment of SFARI Genes
SFARI genes seem to be strongly concentrated in a group of modules and almost abstent in others (many modules with a probability close to 1 and many with a probability close to 0, with not so many in between)
The size of the modules does play a part in the probabilities, with the smallest modules having on average less extreme probabilities than the rest of the modules
SFARI_genes_by_module = dataset %>% mutate('hasSFARIscore' = !gene.score %in% c('None', 'Neuronal')) %>%
group_by(Module, MTcor, hasSFARIscore) %>% summarise(s=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(n=n()), by='Module') %>%
mutate(perc=round(s/n*100,2)) %>% filter(hasSFARIscore & Module != 'gray') %>% arrange(desc(perc)) %>% ungroup
N = sum(SFARI_genes_by_module$n)
S = sum(SFARI_genes_by_module$s)
calc_prob = function(row, log.p, S){
s = row[4] %>% as.numeric
n = row[5] %>% as.numeric
prob = phyper(s, S, N-S, n, log.p = log.p, lower.tail=FALSE)
return(prob)
}
SFARI_genes_by_module$prob = apply(SFARI_genes_by_module, 1, function(x) calc_prob(x, FALSE, S))
SFARI_genes_by_module$adj_prob = p.adjust(SFARI_genes_by_module$prob, method = 'bonferroni')
SFARI_genes_by_module = SFARI_genes_by_module %>% arrange(prob)
ggplotly(SFARI_genes_by_module %>% ggplot(aes(MTcor, prob, size=n)) + geom_point(color=SFARI_genes_by_module$Module, alpha=0.5, aes(id=Module)) +
geom_smooth(color='#cccccc', size = 0.5, se=FALSE) + xlab('Module-Diagnosis Correlation') + ylab('Probability') +
ggtitle(paste0('Corr = ', round(cor(SFARI_genes_by_module$MTcor, SFARI_genes_by_module$prob),2), ': Corr[Module-ASD corr<0] = ',
round(cor(SFARI_genes_by_module$MTcor[SFARI_genes_by_module$MTcor<0], SFARI_genes_by_module$prob[SFARI_genes_by_module$MTcor<0]),3),
' Corr[Module-ASD corr>0] = ',
round(cor(SFARI_genes_by_module$MTcor[SFARI_genes_by_module$MTcor>=0], SFARI_genes_by_module$prob[SFARI_genes_by_module$MTcor>=0]),2))) +
theme_minimal() + theme(legend.position = 'none'))
It’s weird that the Module with the highest (positive) correlation to ASD have less enrichment in SFARI Genes than the rest of the Modules. This seems to be because even though SFARI Genes are quite balanced between over-and under-expressed gemes, they have lower lFC values than the rest of the genes in the over-expressed group
# !diagnostics off
plot_data = genes_info %>% mutate(label = ifelse(!gene.score %in% c('Neuronal', 'None'), 'SFARI', gene.score)) %>% dplyr::select(label, log2FoldChange) %>%
mutate(class = factor(label, levels = c('None', 'Neuronal', 'SFARI')),
quant = cut(log2FoldChange, breaks = quantile(log2FoldChange, probs = seq(0,1,0.05)) %>% as.vector, labels = FALSE)) %>%
filter(label == 'SFARI') %>% group_by(quant) %>% tally %>% ungroup
ggplotly(genes_info %>% mutate(direction = factor(ifelse(log2FoldChange<0, 'under-expressed', 'over-expressed'), levels = c('under-expressed', 'over-expressed')),
label = factor(ifelse(!gene.score %in% c('Neuronal', 'None'), 'SFARI', gene.score), levels = c('None', 'Neuronal', 'SFARI'))) %>%
ggplot(aes(x=direction, fill = label)) + geom_bar(position = 'fill') + ggtitle('Proportion of SFARI Genes for under- and over-expressed genes') +
scale_fill_manual(values = c('#b3b3b3','#808080','#ff6600')) + ylab('Proportion') + xlab('Direction') + scale_y_sqrt() + theme_minimal())
ggplotly(plot_data %>% ggplot(aes(quant, n)) + geom_bar(stat = 'identity', fill = '#ff6600') + geom_smooth(color = 'gray', alpha = 0.3) +
xlab('Log Fold Change Quantiles') + ylab('Number of SFARI Genes') + ggtitle('Number of SFARI Genes for lFC Quantiles') + theme_minimal())
If we separate the SFARI Genes by score we find the same pattern for each score, although much noisier
calc_prob_by_SFARI_score = function(score){
df = dataset %>% filter(gene.score == score) %>% group_by(Module, gene.score) %>% summarise(s = n()) %>%
filter(Module != 'gray') %>% ungroup %>% right_join(SFARI_genes_by_module %>% dplyr::select(Module, MTcor, n), by = 'Module') %>%
mutate(gene.score = '1', color = SFARI_colour_hue(as.numeric(score)), s = ifelse(is.na(s), 0, s)) %>% dplyr::select(Module, MTcor, gene.score, s, n, color)
df$prob = apply(df, 1, function(x) calc_prob(x, FALSE, sum(dataset$gene.score == score)))
df$adj_prob = p.adjust(df$prob, method = 'bonferroni')
return(df)
}
plot_data = c()
for(score in names(table(dataset$gene.score[!dataset$gene.score %in% c('Neuronal','None', 'Others')]))){
score_info = calc_prob_by_SFARI_score(score)
plot_data = rbind(plot_data, score_info)
}
ggplotly(plot_data %>% ggplot(aes(MTcor, prob, size=n, color = color)) + geom_point(alpha=0.5, aes(id=Module)) + geom_smooth(size = 0.5, se=FALSE) +
xlab('Module-Diagnosis Correlation') + scale_colour_manual(values = SFARI_colour_hue(r=1:6)) + ylab('Probability') +
ggtitle('Enrichment by SFARI Gene Score') + theme_minimal() + theme(legend.position = 'none'))
We can interpet the probability we obtain as a p-value (and correct it for multiple testing), we can use it as a threshold to identify modules with a significantly high percentage of SFARI genes (adjusted p-value < 0.01)
Using log-scale to help us differentiate between small differences close to zero better
ggplotly(SFARI_genes_by_module %>% ggplot(aes(MTcor, adj_prob, size=n)) + geom_point(color=SFARI_genes_by_module$Module, alpha=0.5, aes(id=Module)) +
geom_hline(yintercept = 0.05, color = 'gray', linetype = 'dotted') + xlab('Module-Diagnosis Correlation') + ylab('Corrected p-values') + scale_y_log10() +
theme_minimal() + theme(legend.position = 'none'))
top_modules = SFARI_genes_by_module$Module[SFARI_genes_by_module$adj_prob <0.05]
cat(paste0('Keeping top ', length(top_modules),' modules: ', paste(top_modules, collapse = ', ')))
## Keeping top 6 modules: #B79F00, #00A8FF, #FE61CF, #619CFF, #00B9E3, #00BECF
rm(N,S,calc_prob)
The genes belonging to the modules enriched in SARI genes seem to be distributed in all of the PC space except for the highest values of PC2, which we know correspond to over-expressed genes. This agrees with the positive slope at the end of the trend line in the plot above
pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>% left_join(dataset, by='ID') %>%
dplyr::select(ID, PC1, PC2, Module, gene.score) %>% mutate(ImportantModules = ifelse(Module %in% top_modules, as.character(Module), 'Others')) %>%
mutate(color = ifelse(ImportantModules=='Others','gray',ImportantModules), alpha = ifelse(ImportantModules=='Others', 0.1, 0.3))
table(plot_data$ImportantModules)
##
## #00A8FF #00B9E3 #00BECF #619CFF #B79F00 #FE61CF Others
## 270 1733 291 459 174 315 12905
p = plot_data %>% ggplot(aes(PC1, PC2, color=ImportantModules)) + geom_point(alpha=plot_data$alpha) +
scale_colour_manual(values = c(names(table(plot_data$ImportantModules))[-(length(top_modules)+1)],'gray')) +
theme_minimal() + theme(legend.position = 'none') + ggtitle('Modules with the most significant presence of SFARI Genes')
for(tm in top_modules){
p = p + geom_hline(yintercept = mean(plot_data$PC2[plot_data$Module==tm]), color = tm, linetype = 'dashed') +
geom_vline(xintercept = mean(plot_data$PC1[plot_data$Module==tm]), color = tm, linetype = 'dashed')
}
ggExtra::ggMarginal(p, type='density', groupColour = TRUE, size=10)
create_plot = function(module){
plot_data = dataset %>% dplyr::select(ID, paste0('MM.',gsub('#','',module)), GS, gene.score) %>% filter(dataset$Module==module)
colnames(plot_data)[2] = 'Module'
SFARI_colors = as.numeric(names(table(as.character(plot_data$gene.score)[plot_data$gene.score!='None'])))
SFARI_colors = SFARI_colors[!is.na(SFARI_colors)]
p = ggplotly(plot_data %>% ggplot(aes(Module, GS, color=gene.score)) + geom_point(alpha=0.5, aes(ID=ID)) + ylab('Gene Significance') +
scale_color_manual(values=SFARI_colour_hue(r=c(SFARI_colors,8,7))) + theme_minimal() + xlab('Module Membership') +
ggtitle(paste0('Module ', module,' (MTcor = ', round(moduleTraitCor[paste0('ME',module)][[1]],2),')')))
return(p)
}
datTraits = datMeta %>% dplyr::select(Diagnosis)
# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)
# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
names(moduleTraitCor) = colnames(MEs)
create_plot(top_modules[1])
create_plot(top_modules[2])
create_plot(top_modules[3])
create_plot(top_modules[4])
create_plot(top_modules[5])
create_plot(top_modules[6])
rm(datTraits, ME_object, MEs, create_plot)
i = 1
kable(enrichment$enrichmentTable %>% filter(class==top_modules[i]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio, effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[i], ' (SFARI Genes = ',
round(SFARI_genes_by_module$perc[SFARI_genes_by_module$Module==top_modules[i]][1],4), '%)'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000569 | WGCNA humanSpecificOlivedrab2Module frontalCtx FOXP2 | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.0008823 | 0.0000011 | 1.850606 | 168 | 4036 | 80 |
| GO:0051252 | regulation of RNA metabolic process | GO|GO.BP | 0.2041583 | 0.0001640 | 1.881450 | 168 | 3027 | 61 |
| GO:0090304 | nucleic acid metabolic process | GO|GO.BP | 0.2908464 | 0.0002255 | 1.670297 | 168 | 4304 | 77 |
| GO:2000112 | regulation of cellular macromolecule biosynthetic process | GO|GO.BP | 0.2970930 | 0.0002299 | 1.847594 | 168 | 3133 | 62 |
| GO:0019219 | regulation of nucleobase-containing compound metabolic process | GO|GO.BP | 0.3413090 | 0.0002605 | 1.812876 | 168 | 3296 | 64 |
| GO:0010556 | regulation of macromolecule biosynthetic process | GO|GO.BP | 1.0000000 | 0.0007780 | 1.778891 | 168 | 3254 | 62 |
| GO:0031326 | regulation of cellular biosynthetic process | GO|GO.BP | 1.0000000 | 0.0007603 | 1.754842 | 168 | 3405 | 64 |
| GO:0009889 | regulation of biosynthetic process | GO|GO.BP | 1.0000000 | 0.0006901 | 1.748373 | 168 | 3471 | 65 |
| GO:0016070 | RNA metabolic process | GO|GO.BP | 1.0000000 | 0.0010016 | 1.683317 | 168 | 3827 | 69 |
| GO:0005634 | nucleus | GO|GO.CC | 1.0000000 | 0.0013611 | 1.475657 | 168 | 5884 | 93 |
i = 2
kable(enrichment$enrichmentTable %>% filter(class==top_modules[i]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio, effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[i], ' (SFARI Genes = ',
round(SFARI_genes_by_module$perc[SFARI_genes_by_module$Module==top_modules[i]][1],4), '%)'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000188 | CortexWGCNA midfetal M18 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.0007389 | 0.0000010 | 3.949958 | 261 | 426 | 28 |
| JAMiller.AIBS.000349 | Genes bound by KDM5B in MOUSE MESC from PMID 21448134 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.0017535 | 0.0000021 | 1.824771 | 261 | 2964 | 90 |
| JAMiller.AIBS.000161 | Genes increasing with age in fetal PFC | JA Miller at AIBS|Brain|Prenatal brain|Age-associated genes|Cortex | 1.0000000 | 0.0229150 | 2.258634 | 261 | 745 | 28 |
| JAMiller.AIBS.000078 | Cerebellar granule cells | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers | 1.0000000 | 0.0197433 | 1.827103 | 261 | 1513 | 46 |
| GO:2000112 | regulation of cellular macromolecule biosynthetic process | GO|GO.BP | 1.0000000 | 0.0207200 | 1.515342 | 261 | 3133 | 79 |
| GO:0010556 | regulation of macromolecule biosynthetic process | GO|GO.BP | 1.0000000 | 0.0150683 | 1.514399 | 261 | 3254 | 82 |
| JAMiller.AIBS.000569 | WGCNA humanSpecificOlivedrab2Module frontalCtx FOXP2 | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 1.0000000 | 0.0030512 | 1.488994 | 261 | 4036 | 100 |
| GO:0009889 | regulation of biosynthetic process | GO|GO.BP | 1.0000000 | 0.0260383 | 1.471663 | 261 | 3471 | 85 |
| JAMiller.AIBS.000268 | Genes bound by EGR1 in HUMAN ERYTHROLEUKEMIA from PMID 20690147 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.0000000 | 0.0269462 | 1.369208 | 261 | 4828 | 110 |
| GO:0050789 | regulation of biological process | GO|GO.BP | 1.0000000 | 0.0224722 | 1.207686 | 261 | 8957 | 180 |
i = 3
kable(enrichment$enrichmentTable %>% filter(class==top_modules[i]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio, effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[i], ' (SFARI Genes = ',
round(SFARI_genes_by_module$perc[SFARI_genes_by_module$Module==top_modules[i]][1],4), '%)'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000188 | CortexWGCNA midfetal M18 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.0000000 | 0.0000000 | 7.058003 | 313 | 426 | 60 |
| JAMiller.AIBS.000195 | RegionalWGCNA midfetal M25 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.0000000 | 0.0000000 | 8.176139 | 313 | 190 | 31 |
| JAMiller.AIBS.000048 | CortexWGCNA 15-21 post-conception weeks C22 CPenriched enrichedForAutismGenes | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.0000000 | 0.0000000 | 3.962714 | 313 | 607 | 48 |
| JAMiller.AIBS.000044 | CortexWGCNA 15-21 post-conception weeks C18 | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.0000097 | 0.0000000 | 3.140713 | 313 | 718 | 45 |
| JAMiller.AIBS.000268 | Genes bound by EGR1 in HUMAN ERYTHROLEUKEMIA from PMID 20690147 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.0001385 | 0.0000002 | 1.556912 | 313 | 4828 | 150 |
| JAMiller.AIBS.000155 | Lowest in VZ of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 0.0004462 | 0.0000006 | 2.138185 | 313 | 1664 | 71 |
| JAMiller.AIBS.000569 | WGCNA humanSpecificOlivedrab2Module frontalCtx FOXP2 | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.0006891 | 0.0000009 | 1.614107 | 313 | 4036 | 130 |
| JAMiller.AIBS.000257 | Genes bound by DMRT1 in MOUSE TESTES from PMID 23473982 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.0747682 | 0.0000662 | 1.969328 | 313 | 1654 | 65 |
| JAMiller.AIBS.000245 | Genes bound by CREB1 in RAT HIPPOCAMPUS from PMID 23762244 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.1714689 | 0.0001401 | 1.823144 | 313 | 2034 | 74 |
| JAMiller.AIBS.000124 | HippocampusWGCNA yellow | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.3129563 | 0.0002413 | 2.590714 | 313 | 677 | 35 |
i = 4
kable(enrichment$enrichmentTable %>% filter(class==top_modules[i]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio, effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[i], ' (SFARI Genes = ',
round(SFARI_genes_by_module$perc[SFARI_genes_by_module$Module==top_modules[i]][1],4), '%)'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| GO:0090304 | nucleic acid metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.753458 | 451 | 4304 | 217 |
| GO:0016070 | RNA metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.817521 | 451 | 3827 | 200 |
| GO:0010467 | gene expression | GO|GO.BP | 0.00e+00 | 0e+00 | 1.678789 | 451 | 4454 | 215 |
| GO:0005634 | nucleus | GO|GO.CC | 0.00e+00 | 0e+00 | 1.536769 | 451 | 5884 | 260 |
| GO:0003676 | nucleic acid binding | GO|GO.MF | 0.00e+00 | 0e+00 | 1.839548 | 451 | 3214 | 170 |
| GO:0006139 | nucleobase-containing compound metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.610303 | 451 | 4881 | 226 |
| GO:0046483 | heterocycle metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.578302 | 451 | 5002 | 227 |
| GO:0006725 | cellular aromatic compound metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.567335 | 451 | 5037 | 227 |
| GO:0051252 | regulation of RNA metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.826807 | 451 | 3027 | 159 |
| GO:0031981 | nuclear lumen | GO|GO.CC | 0.00e+00 | 0e+00 | 1.709029 | 451 | 3724 | 183 |
| GO:0019219 | regulation of nucleobase-containing compound metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.772679 | 451 | 3296 | 168 |
| JAMiller.AIBS.000349 | Genes bound by KDM5B in MOUSE MESC from PMID 21448134 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1.830435 | 451 | 2964 | 156 |
| GO:0034641 | cellular nitrogen compound metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.507378 | 451 | 5445 | 236 |
| GO:0044428 | nuclear part | GO|GO.CC | 0.00e+00 | 0e+00 | 1.637542 | 451 | 4014 | 189 |
| GO:1901360 | organic cyclic compound metabolic process | GO|GO.BP | 0.00e+00 | 0e+00 | 1.518497 | 451 | 5199 | 227 |
| GO:0010468 | regulation of gene expression | GO|GO.BP | 0.00e+00 | 0e+00 | 1.683595 | 451 | 3615 | 175 |
| GO:0005654 | nucleoplasm | GO|GO.CC | 2.00e-07 | 0e+00 | 1.723000 | 451 | 3169 | 157 |
| GO:0006355 | regulation of transcription, DNA-templated | GO|GO.BP | 3.00e-07 | 0e+00 | 1.785435 | 451 | 2766 | 142 |
| GO:0006351 | transcription, DNA-templated | GO|GO.BP | 8.00e-07 | 0e+00 | 1.740690 | 451 | 2937 | 147 |
| GO:1903506 | regulation of nucleic acid-templated transcription | GO|GO.BP | 9.00e-07 | 0e+00 | 1.759835 | 451 | 2826 | 143 |
| GO:0032774 | RNA biosynthetic process | GO|GO.BP | 9.00e-07 | 0e+00 | 1.729050 | 451 | 2997 | 149 |
| GO:2001141 | regulation of RNA biosynthetic process | GO|GO.BP | 1.10e-06 | 0e+00 | 1.754248 | 451 | 2835 | 143 |
| JAMiller.AIBS.000209 | RegionalWGCNA midfetal M39 AlternativeSplicing | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 1.20e-06 | 0e+00 | 3.058089 | 451 | 580 | 51 |
| GO:0097659 | nucleic acid-templated transcription | GO|GO.BP | 2.80e-06 | 0e+00 | 1.714422 | 451 | 2982 | 147 |
| GO:2000112 | regulation of cellular macromolecule biosynthetic process | GO|GO.BP | 3.50e-06 | 0e+00 | 1.687296 | 451 | 3133 | 152 |
| GO:0031326 | regulation of cellular biosynthetic process | GO|GO.BP | 4.60e-06 | 0e+00 | 1.644435 | 451 | 3405 | 161 |
| GO:1901363 | heterocyclic compound binding | GO|GO.MF | 8.00e-06 | 0e+00 | 1.489880 | 451 | 4832 | 207 |
| GO:0003677 | DNA binding | GO|GO.MF | 1.12e-05 | 0e+00 | 1.947733 | 451 | 1857 | 104 |
| GO:0009889 | regulation of biosynthetic process | GO|GO.BP | 1.12e-05 | 0e+00 | 1.623186 | 451 | 3471 | 162 |
| GO:0010556 | regulation of macromolecule biosynthetic process | GO|GO.BP | 1.72e-05 | 0e+00 | 1.645929 | 451 | 3254 | 154 |
| GO:0097159 | organic cyclic compound binding | GO|GO.MF | 2.51e-05 | 0e+00 | 1.473414 | 451 | 4886 | 207 |
| GO:0034654 | nucleobase-containing compound biosynthetic process | GO|GO.BP | 2.51e-05 | 0e+00 | 1.611310 | 451 | 3475 | 161 |
| GO:0060255 | regulation of macromolecule metabolic process | GO|GO.BP | 2.73e-05 | 0e+00 | 1.469703 | 451 | 4922 | 208 |
| GO:0051171 | regulation of nitrogen compound metabolic process | GO|GO.BP | 3.59e-05 | 1e-07 | 1.485616 | 451 | 4682 | 200 |
| GO:0044451 | nucleoplasm part | GO|GO.CC | 3.83e-05 | 1e-07 | 2.388518 | 451 | 961 | 66 |
| GO:0043170 | macromolecule metabolic process | GO|GO.BP | 3.99e-05 | 1e-07 | 1.315338 | 451 | 7562 | 286 |
| GO:0018130 | heterocycle biosynthetic process | GO|GO.BP | 4.45e-05 | 1e-07 | 1.596509 | 451 | 3529 | 162 |
| GO:0019438 | aromatic compound biosynthetic process | GO|GO.BP | 5.00e-05 | 1e-07 | 1.594250 | 451 | 3534 | 162 |
i = 5
kable(enrichment$enrichmentTable %>% filter(class==top_modules[i]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio, effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[i], ' (SFARI Genes = ',
round(SFARI_genes_by_module$perc[SFARI_genes_by_module$Module==top_modules[i]][1],4), '%)'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000052 | CortexWGCNA 15-21 post-conception weeks C26 | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 2.708083 | 1695 | 721 | 211 |
| JAMiller.AIBS.000142 | Highest in CP of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 2.194883 | 1695 | 1210 | 287 |
| JAMiller.AIBS.000569 | WGCNA humanSpecificOlivedrab2Module frontalCtx FOXP2 | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 1.524703 | 1695 | 4036 | 665 |
| JAM:002769 | downAD_mitochondrion | JAM|BrainLists|BrainLists.Blalock_AD | 0.00e+00 | 0e+00 | 3.364977 | 1695 | 264 | 96 |
| JAMiller.AIBS.000150 | Highest in CP of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 1.878513 | 1695 | 1266 | 257 |
| JAM:003016 | downAD_synapticTransmission | JAM|BrainLists|BrainLists.Blalock_AD | 0.00e+00 | 0e+00 | 5.152621 | 1695 | 88 | 49 |
| JAMiller.AIBS.000155 | Lowest in VZ of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 0.00e+00 | 0e+00 | 1.723944 | 1695 | 1664 | 310 |
| JAMiller.AIBS.000123 | HippocampusWGCNA turquoise DGenriched upAge | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.00e+00 | 0e+00 | 1.921531 | 1695 | 1098 | 228 |
| JAMiller.AIBS.000570 | WGCNA Olivedrab2ModuleGenes with enriched ELAVL2 targets | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 2.481758 | 1695 | 481 | 129 |
| JAM:003072 | Tail of Caudate Nucleus_IN_Striatum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.506055 | 1695 | 161 | 61 |
| GO:0044456 | synapse part | GO|GO.CC | 0.00e+00 | 0e+00 | 1.967674 | 1695 | 823 | 175 |
| GO:0045202 | synapse | GO|GO.CC | 0.00e+00 | 0e+00 | 1.834783 | 1695 | 1044 | 207 |
| GO:0097060 | synaptic membrane | GO|GO.CC | 0.00e+00 | 0e+00 | 2.418297 | 1695 | 375 | 98 |
| JAMiller.AIBS.000141 | CP enriched in E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 2.112789 | 1695 | 565 | 129 |
| GO:0097458 | neuron part | GO|GO.CC | 0.00e+00 | 0e+00 | 1.651046 | 1695 | 1418 | 253 |
| JAM:002764 | downAging_mitochondria_synapse | JAM|BrainLists|BrainLists.Lu_Aging | 0.00e+00 | 0e+00 | 2.349013 | 1695 | 390 | 99 |
| JAM:002751 | Basal Pons | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.196728 | 1695 | 165 | 57 |
| JAM:002744 | Autism_differential_expression_across_at_least_one_comparison | JAM|BrainLists|BrainLists.Voineagu | 0.00e+00 | 0e+00 | 1.855589 | 1695 | 763 | 153 |
| JAM:003054 | subiculum_IN_Hippocampal Formation | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.065639 | 1695 | 163 | 54 |
| JAMiller.AIBS.000005 | CPi markers at 21 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Postmitotic brain | 1.00e-07 | 0e+00 | 2.395777 | 1695 | 309 | 80 |
| JAM:002805 | Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 2.00e-07 | 0e+00 | 2.988769 | 1695 | 161 | 52 |
| JAM:002739 | arcuate nucleus of medulla_IN_Myelencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 2.00e-07 | 0e+00 | 2.936799 | 1695 | 167 | 53 |
| JAM:002920 | Lateral Nucleus_IN_Amygdala | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 5.00e-07 | 0e+00 | 2.916314 | 1695 | 165 | 52 |
| GO:0099536 | synaptic signaling | GO|GO.BP | 5.00e-07 | 0e+00 | 1.949884 | 1695 | 560 | 118 |
| GO:0034220 | ion transmembrane transport | GO|GO.BP | 7.00e-07 | 0e+00 | 1.720897 | 1695 | 898 | 167 |
| GO:0098793 | presynapse | GO|GO.CC | 1.00e-06 | 0e+00 | 2.108154 | 1695 | 417 | 95 |
| JAMiller.AIBS.000095 | Cortical PNOC neurons | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers|Cortex | 2.50e-06 | 0e+00 | 1.282364 | 1695 | 3940 | 546 |
| GO:0045211 | postsynaptic membrane | GO|GO.CC | 3.10e-06 | 0e+00 | 2.346005 | 1695 | 284 | 72 |
| GO:0099537 | trans-synaptic signaling | GO|GO.BP | 3.40e-06 | 0e+00 | 1.917431 | 1695 | 555 | 115 |
| JAM:002882 | Hippocampal Formation | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 4.00e-06 | 0e+00 | 2.809155 | 1695 | 168 | 51 |
| GO:0007268 | chemical synaptic transmission | GO|GO.BP | 4.10e-06 | 0e+00 | 1.918037 | 1695 | 550 | 114 |
| GO:0098916 | anterograde trans-synaptic signaling | GO|GO.BP | 4.10e-06 | 0e+00 | 1.918037 | 1695 | 550 | 114 |
| JAMiller.AIBS.000463 | Genes bound by SMAD4 in HUMAN A2780 from PMID 21799915 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 9.70e-06 | 0e+00 | 1.415427 | 1695 | 2079 | 318 |
| GO:0043005 | neuron projection | GO|GO.CC | 1.07e-05 | 0e+00 | 1.625648 | 1695 | 1036 | 182 |
| GO:0030424 | axon | GO|GO.CC | 1.10e-05 | 0e+00 | 1.942358 | 1695 | 505 | 106 |
| GO:0022836 | gated channel activity | GO|GO.MF | 2.52e-05 | 0e+00 | 2.420796 | 1695 | 237 | 62 |
| JAMiller.AIBS.000042 | CortexWGCNA 15-21 post-conception weeks C16 SPenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 2.81e-05 | 0e+00 | 2.570469 | 1695 | 198 | 55 |
| JAM:002824 | Dentate Nucleus_IN_Cerebellar Nucleus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 5.67e-05 | 1e-07 | 2.725012 | 1695 | 163 | 48 |
| GO:0034702 | ion channel complex | GO|GO.CC | 5.72e-05 | 1e-07 | 2.437355 | 1695 | 224 | 59 |
| GO:0005216 | ion channel activity | GO|GO.MF | 6.05e-05 | 1e-07 | 2.256199 | 1695 | 283 | 69 |
| GO:0022839 | ion gated channel activity | GO|GO.MF | 6.28e-05 | 1e-07 | 2.391843 | 1695 | 236 | 61 |
| GO:1902495 | transmembrane transporter complex | GO|GO.CC | 7.84e-05 | 1e-07 | 2.361023 | 1695 | 243 | 62 |
| GO:0099240 | intrinsic component of synaptic membrane | GO|GO.CC | 8.40e-05 | 1e-07 | 2.764088 | 1695 | 154 | 46 |
i = 6
kable(enrichment$enrichmentTable %>% filter(class==top_modules[i]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio, effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[i], ' (SFARI Genes = ',
round(SFARI_genes_by_module$perc[SFARI_genes_by_module$Module==top_modules[i]][1],4), '%)'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000349 | Genes bound by KDM5B in MOUSE MESC from PMID 21448134 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.0121595 | 0.0000127 | 1.745180 | 282 | 2964 | 93 |
| JAMiller.AIBS.000209 | RegionalWGCNA midfetal M39 AlternativeSplicing | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.0761661 | 0.0000671 | 2.972823 | 282 | 580 | 31 |
| JAMiller.AIBS.000183 | CortexWGCNA midfetal M13 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.7377701 | 0.0005196 | 2.086836 | 282 | 1306 | 49 |
| GO:0022613 | ribonucleoprotein complex biogenesis | GO|GO.BP | 1.0000000 | 0.0056972 | 3.183105 | 282 | 332 | 19 |
| GO:0000375 | RNA splicing, via transesterification reactions | GO|GO.BP | 1.0000000 | 0.0374287 | 3.016709 | 282 | 295 | 16 |
| JAMiller.AIBS.000211 | RegionalWGCNA midfetal M41 mRNA metabolism | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 1.0000000 | 0.0223577 | 2.739929 | 282 | 406 | 20 |
| GO:0006397 | mRNA processing | GO|GO.BP | 1.0000000 | 0.0187850 | 2.697533 | 282 | 433 | 21 |
| GO:0016071 | mRNA metabolic process | GO|GO.BP | 1.0000000 | 0.0265071 | 2.200541 | 282 | 733 | 29 |
| JAMiller.AIBS.000518 | Genes bound by TCF4 in HUMAN U87 from PMID 23295773 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.0000000 | 0.0021888 | 1.587577 | 282 | 3013 | 86 |
| JAMiller.AIBS.000332 | Genes bound by HNF4A in human HepG2 from PMID 19822575 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.0000000 | 0.0503541 | 1.319920 | 282 | 5141 | 122 |
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] BrainDiseaseCollection_1.00
## [2] anRichment_1.01-2
## [3] TxDb.Mmusculus.UCSC.mm10.knownGene_3.4.7
## [4] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [5] GenomicFeatures_1.36.4
## [6] GenomicRanges_1.36.1
## [7] GenomeInfoDb_1.20.0
## [8] anRichmentMethods_0.90-1
## [9] WGCNA_1.69
## [10] fastcluster_1.1.25
## [11] dynamicTreeCut_1.63-1
## [12] GO.db_3.8.2
## [13] AnnotationDbi_1.46.1
## [14] IRanges_2.18.3
## [15] S4Vectors_0.22.1
## [16] Biobase_2.44.0
## [17] BiocGenerics_0.30.0
## [18] biomaRt_2.40.5
## [19] knitr_1.28
## [20] doParallel_1.0.15
## [21] iterators_1.0.12
## [22] foreach_1.5.0
## [23] polycor_0.7-10
## [24] expss_0.10.2
## [25] GGally_1.5.0
## [26] gridExtra_2.3
## [27] viridis_0.5.1
## [28] viridisLite_0.3.0
## [29] RColorBrewer_1.1-2
## [30] dendextend_1.13.4
## [31] plotly_4.9.2
## [32] glue_1.3.2
## [33] reshape2_1.4.3
## [34] forcats_0.5.0
## [35] stringr_1.4.0
## [36] dplyr_0.8.5
## [37] purrr_0.3.3
## [38] readr_1.3.1
## [39] tidyr_1.0.2
## [40] tibble_3.0.0
## [41] ggplot2_3.3.0
## [42] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5
## [3] Hmisc_4.4-0 plyr_1.8.6
## [5] lazyeval_0.2.2 splines_3.6.3
## [7] crosstalk_1.1.0.1 BiocParallel_1.18.1
## [9] digest_0.6.25 htmltools_0.4.0
## [11] fansi_0.4.1 magrittr_1.5
## [13] checkmate_2.0.0 memoise_1.1.0
## [15] cluster_2.1.0 annotate_1.62.0
## [17] Biostrings_2.52.0 modelr_0.1.6
## [19] matrixStats_0.56.0 prettyunits_1.1.1
## [21] jpeg_0.1-8.1 colorspace_1.4-1
## [23] blob_1.2.1 rvest_0.3.5
## [25] haven_2.2.0 xfun_0.12
## [27] crayon_1.3.4 RCurl_1.98-1.1
## [29] jsonlite_1.6.1 genefilter_1.66.0
## [31] impute_1.58.0 survival_3.1-12
## [33] gtable_0.3.0 zlibbioc_1.30.0
## [35] XVector_0.24.0 DelayedArray_0.10.0
## [37] scales_1.1.0 mvtnorm_1.1-0
## [39] DBI_1.1.0 miniUI_0.1.1.1
## [41] Rcpp_1.0.4 xtable_1.8-4
## [43] progress_1.2.2 htmlTable_1.13.3
## [45] foreign_0.8-76 bit_1.1-15.2
## [47] preprocessCore_1.46.0 Formula_1.2-3
## [49] htmlwidgets_1.5.1 httr_1.4.1
## [51] acepack_1.4.1 ellipsis_0.3.0
## [53] farver_2.0.3 pkgconfig_2.0.3
## [55] reshape_0.8.8 XML_3.99-0.3
## [57] nnet_7.3-14 dbplyr_1.4.2
## [59] locfit_1.5-9.4 later_1.0.0
## [61] labeling_0.3 tidyselect_1.0.0
## [63] rlang_0.4.5 munsell_0.5.0
## [65] cellranger_1.1.0 tools_3.6.3
## [67] cli_2.0.2 generics_0.0.2
## [69] RSQLite_2.2.0 broom_0.5.5
## [71] fastmap_1.0.1 evaluate_0.14
## [73] yaml_2.2.1 bit64_0.9-7
## [75] fs_1.4.0 nlme_3.1-147
## [77] mime_0.9 ggExtra_0.9
## [79] xml2_1.2.5 compiler_3.6.3
## [81] rstudioapi_0.11 png_0.1-7
## [83] reprex_0.3.0 geneplotter_1.62.0
## [85] stringi_1.4.6 highr_0.8
## [87] lattice_0.20-41 Matrix_1.2-18
## [89] vctrs_0.2.4 pillar_1.4.3
## [91] lifecycle_0.2.0 data.table_1.12.8
## [93] bitops_1.0-6 httpuv_1.5.2
## [95] rtracklayer_1.44.4 R6_2.4.1
## [97] latticeExtra_0.6-29 promises_1.1.0
## [99] codetools_0.2-16 assertthat_0.2.1
## [101] SummarizedExperiment_1.14.1 DESeq2_1.24.0
## [103] withr_2.1.2 GenomicAlignments_1.20.1
## [105] Rsamtools_2.0.3 GenomeInfoDbData_1.2.1
## [107] mgcv_1.8-31 hms_0.5.3
## [109] grid_3.6.3 rpart_4.1-15
## [111] rmarkdown_2.1 shiny_1.4.0.2
## [113] lubridate_1.7.4 base64enc_0.1-3